1. Field
This disclosure is generally related to task management. More specifically, this disclosure is related to using document-usage footprints to estimate user tasks.
2. Related Art
Modern-day workers often found themselves juggling multiple tasks and activities. Many task-management systems have been developed to assist these multitasking efforts. Task-management systems typically provide some efficient way of switching from one task to another. In order to facilitate task management and task switching, a task-management system needs to have knowledge of how a user's overall workspace is conceptually partitioned into the individual constituent tasks. Note that performing a task often involves the use of multiple applications, documents, and communication mechanisms with others.
One common problem facing the task-management system is to determine which documents or applications are associated with each task. For example, in order to assist a user with task switching, the system needs to recognize that task switching has occurred when the user opens a document belonging to a different task.
Conventional task-detection methods either require high amounts of user feedback, or provide a rather imprecise representation of a user's task. For example, some task-management systems rely on explicit user input for such knowledge, thus generating an extra burden for users. Some task-detection methods automatically learn a user's tasks in a supervised manner, which requires a user to provide task names/labels constantly in order to train the system. Due to the large amount of “extra work” involved in setting up such systems, normal users tend to reject such approaches. In contrast, unsupervised approaches do not require any feedback from users, but generally provide a poor task-detection result.